CN111537845A - Method for identifying aging state of oil paper insulation equipment based on Raman spectrum cluster analysis - Google Patents
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- 238000009413 insulation Methods 0.000 title claims abstract description 174
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- 238000003878 thermal aging Methods 0.000 claims abstract description 13
- 238000002474 experimental method Methods 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims abstract description 10
- 239000003921 oil Substances 0.000 claims description 81
- 239000011159 matrix material Substances 0.000 claims description 24
- 238000001069 Raman spectroscopy Methods 0.000 claims description 17
- 239000002480 mineral oil Substances 0.000 claims description 16
- 235000010446 mineral oil Nutrition 0.000 claims description 16
- 230000003595 spectral effect Effects 0.000 claims description 13
- 230000005540 biological transmission Effects 0.000 claims description 7
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000010438 heat treatment Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000011410 subtraction method Methods 0.000 claims description 4
- 238000001035 drying Methods 0.000 claims description 3
- 229910052757 nitrogen Inorganic materials 0.000 claims description 3
- 238000002791 soaking Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 10
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- 238000003745 diagnosis Methods 0.000 description 4
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- 239000000126 substance Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000004817 gas chromatography Methods 0.000 description 2
- 241000934806 Krameria Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 230000007123 defense Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
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- 239000002655 kraft paper Substances 0.000 description 1
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- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
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Abstract
The invention provides a method for identifying the aging state of oil paper insulation equipment based on Raman spectrum cluster analysis, which comprises the following steps: respectively obtaining oil paper insulation samples of a plurality of known aging stages and unknown aging stages through an oil paper insulation thermal aging experiment; acquiring Raman spectrum data of the oiled paper insulation samples in a plurality of known aging stages and unknown aging stages, and carrying out standardization treatment; extracting the Raman spectrum characteristic quantity of the oil paper insulation sample at the known aging stage after standardized treatment by adopting a principal component analysis method, and establishing an oil paper insulation aging evaluation model based on a cluster analysis method; and inputting the Raman spectrum characteristic quantity of the oiled paper insulation sample in the unknown aging stage into the model for clustering analysis to obtain the aging stage of the oiled paper insulation sample in the unknown aging stage. The invention establishes the oil paper insulation aging evaluation model based on Raman spectrum measurement to analyze the aging state, and can quickly and effectively diagnose the overall aging state of the on-site oil paper insulation equipment.
Description
Technical Field
The invention relates to the technical field of electrical equipment safety state evaluation, in particular to an oil paper insulation equipment aging state identification method based on Raman spectrum cluster analysis.
Background
The safe and reliable operation of the electrical equipment is the first line of defense for avoiding major accidents of the power grid, and the oil paper insulation equipment is an important component in the power grid. The problem that the Clarity No. 25 mineral oil paper is aged can be brought to long-term operation of the oil paper insulation equipment, equipment faults are easily caused, and the reliable operation of a power system is influenced. Therefore, the aging degree of the oil paper insulating material is accurately diagnosed, the aging state of the oil paper insulating equipment is mastered in time, a basis can be provided for the insulation state and the whole life cycle management of the oil paper insulating equipment, and the safe operation of a power grid is ensured.
At present, methods for detecting the aging state of the oiled paper insulation equipment mainly comprise High Performance Liquid Chromatography (HPLC), Gas Chromatography (GC) and the like, but the methods can only perform measurement on limited substances, and the methods need more pretreatment work before measurement, so that the time is long.
In the prior art, a Raman spectrum is generally applicable to the field of material composition analysis and state diagnosis, and an oil paper insulation Raman spectrum analysis platform set up in a laboratory can be combined, so that an oil paper insulation sample obtained by an accelerated heat aging experiment is divided into four stages of good insulation, an aging initial stage, an aging middle stage and an aging final stage according to the average polymerization degree of insulation paper. Although the aging process of the oil paper insulation sample has obvious regularity, namely the aging degree is increased continuously along with the increase of time, the method cannot obtain the relation between the specific aging degree and the aging time, and the aging degree of different oil paper insulation samples is different in the same aging time. Therefore, the degree of polymerization of the insulating paper can only be used as a judgment standard in an experimental process, but not as a judgment index in practical application, and the specific aging stage of the oil paper insulating sample cannot be accurately obtained, especially the connection points at different aging stages, and the aging stage of the oil paper insulating sample is more difficult to distinguish.
Disclosure of Invention
The invention provides a method for identifying the aging state of oil paper insulation equipment based on Raman spectrum cluster analysis, which utilizes the superiority of cluster analysis in the aspects of processing uncertainty and inaccuracy and combines Raman spectrum to classify oil paper insulation samples so as to solve the problems that the specific aging stage of the oil paper insulation samples cannot be accurately obtained and the aging state of the oil paper insulation equipment in practical application cannot be judged in the prior art.
The invention provides an oil paper insulation equipment aging state identification method based on Raman spectrum cluster analysis, which specifically comprises the following steps:
obtaining oil paper insulation samples of a plurality of known aging stages through an oil paper insulation thermal aging experiment;
acquiring Raman spectrum data of a plurality of oiled paper insulation samples in the known aging stage, and carrying out standardization treatment;
extracting the Raman spectrum characteristic quantity of the oil paper insulation sample in the known aging stage after standardized treatment by adopting a principal component analysis method, and establishing an oil paper insulation aging evaluation model based on a cluster analysis method;
obtaining oil paper insulation samples of a plurality of unknown aging stages through an oil paper insulation thermal aging experiment;
acquiring Raman spectrum data of the oil paper insulation samples in the unknown aging stages, and carrying out standardization treatment;
and extracting the Raman spectrum characteristic quantity of the oil paper insulation sample in the unknown aging stage after the standardization treatment by adopting a principal component analysis method, and inputting the Raman spectrum characteristic quantity into the oil paper insulation aging evaluation model for clustering analysis to obtain the aging stage of the oil paper insulation sample in the unknown aging stage.
In the technical scheme, the original Raman spectrum data are subjected to dimensionality reduction through a principal component analysis method, the Raman spectrum data subjected to dimensionality reduction are used as characteristic quantities to perform cluster analysis, an oil paper insulation aging evaluation model is established to perform aging state analysis, and diagnosis of the overall aging state of on-site oil paper insulation equipment can be quickly and effectively achieved.
Optionally, the step of obtaining the oiled paper insulation samples at a plurality of known aging stages through an oiled paper insulation thermal aging experiment includes the following steps:
drying Clarithrome No. 25 mineral oil and 0.2mm insulating paper in a vacuum oven at 90 deg.C for 48 hr;
adjusting the temperature of the vacuum box to 60 ℃, and soaking the insulation paper in the mineral oil Clarity No. 25 for 24 hours;
moving the oiled paper insulation sample into a vacuum heating tank filled with nitrogen;
the heating tank was placed in a constant temperature oil bath at 130 ℃ and subjected to accelerated thermal aging for 25 days.
Optionally, the moisture content of the Clarithrome No. 25 mineral oil is less than 10mg/kg, and the moisture content of the insulating paper is less than 0.5%.
Optionally, the mass ratio of the insulating paper to the kramaye No. 25 mineral oil is 1: 10.
Optionally, the normalization processing formula is as follows:
wherein x is*Is normalized Raman spectrum data value, x is original Raman spectrum data value, xminIs the minimum value, x, of the Raman spectral datamaxIs the maximum of the raman spectral data.
By adopting the technical scheme, the influence of Raman spectrum measurement errors can be eliminated, and the data of different samples have comparability.
Optionally, the operation method of the principal component analysis method is as follows:
performing linear fitting on a plurality of independent variables contained in the original Raman spectrum data by using a maximum variance principle;
and replacing the original high-dimensional variable with the new low-dimensional variable to realize the dimension reduction of the Raman spectrum data.
Optionally, the step of establishing the oiled paper insulation aging evaluation model includes:
extracting n Raman spectrum characteristic quantities of the oiled paper insulation sample at the known aging stage by a principal component analysis method, and modeling;
performing cluster analysis by taking known different aging stages as row variables and n Raman spectrum characteristic quantities as column variables;
and clustering according to a net-editing method, and connecting elements which are larger than a threshold lambda in the transmission closure matrix according to a clustering principle, so that the elements on one line belong to the same class.
Optionally, the process of cluster analysis is as follows:
let U be the paper oil insulation samples of m known aging stages to be classified, wherein each paper oil insulation sample of the known aging stages to be classified is characterized by a set of data, and the formula is as follows:
Ui＝(xi1+xi2+xi3+...+xin)，
wherein, UiDenotes the ith oiled paper insulation sample, xi11 st Raman spectrum characteristic quantity, x, of the ith oiled paper insulation sample i22 nd Raman spectrum characteristic quantity, x, of the ith oiled paper insulation samplei3Represents the 3 rd Raman spectrum characteristic quantity of the ith oiled paper insulation sample,xinrepresenting the nth Raman spectrum characteristic quantity of the ith oiled paper insulation sample;
the similarity relation is established by using an absolute value subtraction method, and the method for calculating the similarity coefficient comprises the following steps:
wherein i and j are the numbers of the oiled paper insulation samples, rijThe similarity coefficient of the ith oiled paper insulation sample and the jth oiled paper insulation sample is shown, c is a constant, xikIs the k Raman spectrum characteristic quantity, x, of the ith oiled paper insulation samplejkThe kth Raman spectrum characteristic quantity of the jth oilpaper insulation sample is obtained;
taking the similarity coefficient of the oiled paper insulation samples in each known aging stage as an element of a similarity matrix, and obtaining a similarity matrix R;
method for solving transmission closure matrix R by using flat method16The calculation formula is as follows:
R2＝R*R＝∨(R(a,b)∧R(b,c))，
wherein a, b and c all represent elements in the similarity matrix R, and R can be obtained by the same method4，R8，R16。
Optionally, the performing, by clustering, the raman spectrum characteristic quantity of the oilpaper insulation sample at the unknown aging stage specifically includes:
adding the Raman spectrum characteristic quantity of the oiled paper insulation sample at the unknown aging stage into an oiled paper insulation aging evaluation model as a new line variable;
performing cluster analysis on the new row variable based on a cluster analysis method;
and obtaining the aging stage of the oiled paper insulation sample in the unknown aging stage according to the clustering result.
Compared with the prior art, the invention has the following beneficial effects:
(1) the method is based on the Raman spectrum technology, and can quickly and effectively realize the judgment of the integral aging state of the on-site oil paper insulation equipment through the cluster analysis of the aging sample; the Raman spectrum detection is non-contact measurement, continuous measurement can be realized, the influence factor of the environment is small, online monitoring can be realized more easily, aging trace characteristic substances in the oil paper insulation equipment have Raman activity and can be subjected to Raman detection, the obvious superiority of cluster analysis in the aspects of processing uncertainty and inaccuracy is combined, the classification problem of the oil paper insulation sample can be solved more effectively, and therefore diagnosis and evaluation of the aging state of the oil paper insulation sample can be performed better.
(2) The invention does not need experimental pretreatment work adopted in the prior art, does not need training an oil paper insulation aging evaluation model, and can more efficiently realize the aging state judgment of the oil paper insulation equipment.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph showing the raw Raman spectrum data of 16 oiled paper insulation samples according to the embodiment of the present invention;
FIG. 3 is a diagram illustrating principal components analysis results according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a similarity matrix according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a transitive closure matrix according to an embodiment of the present invention;
fig. 6 is a diagram of a cluster analysis result according to an embodiment of the present invention.
Detailed Description
Referring to fig. 1, the method for identifying the aging state of the oil paper insulation equipment based on raman spectrum cluster analysis provided by the invention specifically comprises the following steps:
obtaining oil paper insulation samples of a plurality of known aging stages through an oil paper insulation thermal aging experiment;
acquiring Raman spectrum data of a plurality of oiled paper insulation samples in the known aging stage, and carrying out standardization treatment;
extracting the Raman spectrum characteristic quantity of the oil paper insulation sample in the known aging stage after standardized treatment by adopting a principal component analysis method, and establishing an oil paper insulation aging evaluation model based on a cluster analysis method;
obtaining oil paper insulation samples of a plurality of unknown aging stages through an oil paper insulation thermal aging experiment;
acquiring Raman spectrum data of the oil paper insulation samples in the unknown aging stages, and carrying out standardization treatment;
and extracting the Raman spectrum characteristic quantity of the oil paper insulation sample in the unknown aging stage after the standardization treatment by adopting a principal component analysis method, and inputting the Raman spectrum characteristic quantity into the oil paper insulation aging evaluation model for clustering analysis to obtain the aging stage of the oil paper insulation sample in the unknown aging stage.
In the technical scheme, the original Raman spectrum data are subjected to dimensionality reduction through a principal component analysis method, the Raman spectrum data subjected to dimensionality reduction are used as characteristic quantities to perform cluster analysis, an oil paper insulation aging evaluation model is established to perform aging state analysis, and diagnosis of the overall aging state of on-site oil paper insulation equipment can be quickly and effectively achieved.
On the basis of the above specific embodiment, further, the obtaining of the oiled paper insulation samples at several known aging stages through the oiled paper insulation heat aging test comprises the following steps:
drying Clarithrome No. 25 mineral oil and 0.2mm insulating paper in a vacuum oven at 90 deg.C for 48 hr;
adjusting the temperature of the vacuum box to 60 ℃, and soaking the insulation paper in the mineral oil Clarity No. 25 for 24 hours;
moving the oiled paper insulation sample into a vacuum heating tank filled with nitrogen;
the heating tank was placed in a constant temperature oil bath at 130 ℃ and subjected to accelerated thermal aging for 25 days.
In addition to the above embodiment, the moisture content of the krameria 25 mineral oil is 10mg/kg or less, and the moisture content of the insulating paper is 0.5% or less.
On the basis of the above embodiment, further, the mass ratio of the insulating paper to the kramaye No. 25 mineral oil is 1: 10.
On the basis of the foregoing embodiment, further, the normalization processing formula is as follows:
wherein x is*Is normalized Raman spectrum data value, x is original Raman spectrum data value, xminIs the minimum value, x, of the Raman spectral datamaxIs the maximum of the raman spectral data.
By adopting the technical scheme, the influence of Raman spectrum measurement errors can be eliminated, and the data of different samples have comparability.
In addition to the above embodiments, the principal component analysis method may further include:
performing linear fitting on a plurality of independent variables contained in the original Raman spectrum data by using a maximum variance principle;
and replacing the original high-dimensional variable with the new low-dimensional variable to realize the dimension reduction of the Raman spectrum data.
On the basis of the above specific embodiment, further, the step of establishing the oiled paper insulation aging evaluation model includes:
extracting n Raman spectrum characteristic quantities of the oiled paper insulation sample at the known aging stage by a principal component analysis method, and modeling;
performing cluster analysis by taking known different aging stages as row variables and n Raman spectrum characteristic quantities as column variables;
and clustering according to a net-editing method, and connecting elements which are larger than a threshold lambda in the transmission closure matrix according to a clustering principle, so that the elements on one line belong to the same class.
On the basis of the foregoing specific embodiments, further, the process of cluster analysis is as follows:
let U be the paper oil insulation samples of m known aging stages to be classified, wherein each paper oil insulation sample of the known aging stages to be classified is characterized by a set of data, and the formula is as follows:
Ui＝(xi1+xi2+xi3+...+xin)，
wherein, UiDenotes the ith oiled paper insulation sample, xi11 st Raman spectrum characteristic quantity, x, of the ith oiled paper insulation sample i22 nd Raman spectrum characteristic quantity, x, of the ith oiled paper insulation samplei3Represents the 3 rd Raman spectrum characteristic quantity, x of the ith oiled paper insulation sampleinRepresenting the nth Raman spectrum characteristic quantity of the ith oiled paper insulation sample;
the similarity relation is established by using an absolute value subtraction method, and the method for calculating the similarity coefficient comprises the following steps:
wherein i and j are the numbers of the oiled paper insulation samples, rijThe similarity coefficient of the ith oiled paper insulation sample and the jth oiled paper insulation sample is shown, c is a constant, xikIs the k Raman spectrum characteristic quantity, x, of the ith oiled paper insulation samplejkThe kth Raman spectrum characteristic quantity of the jth oilpaper insulation sample is obtained;
taking the similarity coefficient of the oiled paper insulation samples in each known aging stage as an element of a similarity matrix, and obtaining a similarity matrix R;
method for solving transmission closure matrix R by using flat method16The calculation formula is as follows:
R2＝R*R＝∨(R(a,b)∧R(b,c))，
wherein a, b and c all represent elements in the similarity matrix R, and R can be obtained by the same method4，R8，R16。
On the basis of the foregoing specific embodiment, further, the performing a cluster analysis on the raman spectral feature quantity of the oiled paper insulation sample at the unknown aging stage specifically includes:
adding the Raman spectrum characteristic quantity of the oiled paper insulation sample at the unknown aging stage into an oiled paper insulation aging evaluation model as a new line variable;
performing cluster analysis on the new row variable based on a cluster analysis method;
and obtaining the aging stage of the oiled paper insulation sample in the unknown aging stage according to the clustering result.
Examples
First, 16 oiled paper insulation samples of different aging stages, i.e., U is 16, are obtained by an oiled paper insulation heat aging test at 130 ℃, and the oiled paper insulation samples of 16 different aging stages are respectively 1d, 2d, 3d, 4d, 5d, 6d, 7d, 8d, 10d, 12d, 14d, 16d, 18d, 20d, 22d and 24d, which are respectively represented as U1、U2、U3、U4、U5、U6、U7、U8、U9、U10、U11、U12、U13、U14、U15、U16(ii) a And according to the polymerization degree of the insulating paper, dividing 16 oil paper insulating samples into four different aging stages with good insulation, namely 1d-4d, 5d-8d in the initial aging stage, 10d-16d in the middle aging stage and 18d-24d in the final aging stage. In this embodiment, the insulating paper used in the oil-paper insulating thermal aging test is kraft paper.
Next, raman spectrum detection is performed on the 16 oiled paper insulation samples in the known aging stages, and the 16 oiled paper insulation samples in the known aging stages are numbered, and as shown in fig. 2, raman spectrum data of the oiled paper insulation samples in the known aging stages are acquired. In order to eliminate the influence of errors in the raman spectroscopy measurement, in this embodiment, the mean value of the results of 5 times of repeated measurements of each sample is used as the raman spectroscopy data of the oiled paper insulation sample with known aging stage, and the result is shown in fig. 2.
As the fluorescent substance, the impurities in the Clarity 25 mineral oil and the fluorescence of the Clarity 25 mineral oil are generated in the aging process of the oil paper insulation equipment, the base line interference generally exists in Raman spectrum signals, and the base line interference can greatly influence the extraction of Raman spectrum characteristic quantity. In addition, the original raman spectrum signal of the mineral oil Clarity No. 25 is 1023 data points, and the data dimensionality is too high, so that the calculation amount is too large, the algorithm is difficult to optimize, and even dimension disaster is caused. Therefore, the raman spectrum data needs to be preprocessed, including removing the baseline by using a cubic spline function, reducing the raman spectrum noise by using a five-point cubic smoothing algorithm, and finally performing normalization processing on the raman spectrum data.
As shown in fig. 3, after the raman spectrum data preprocessing work is completed, a Principal Component Analysis (PCA) method is used to perform dimensionality reduction on the raman spectrum data on the premise of maximally retaining the diagnostic information, the PCA analysis result is shown in fig. 3, the cumulative contribution rate of the first six principal components in fig. 3 reaches 98%, the spectrum information of the original raman spectrum data can be fully expressed, the first six principal components extracted from each oil paper insulation sample are used as raman spectrum characteristic quantities for establishing an oil paper insulation aging evaluation model, the first six principal components of the extracted 16 oil paper insulation samples are shown in table 1 below, and table 1 is the number values of the first six principal components of each of 16 oil paper insulation samples; PC1, PC2, PC3, PC4, PC5 and PC6 in Table 1 respectively represent a first main component, a second main component, a third main component, a fourth main component, a fifth main component and a sixth main component, and serial numbers 1-16 of the oil paper insulation samples to be classified in Table 1 respectively correspond to U1-U16The oiled paper insulation sample of (1).
TABLE 1
As shown in fig. 4, similarity coefficients of raman spectrum characteristic quantities of 16 oil paper insulation samples form a similarity matrix R, in order to construct the similarity matrix R, it is necessary to determine a similarity relationship between six raman spectrum characteristic quantities of 16 oil paper insulation samples, and calculate a similarity coefficient between each oil paper insulation sample, where a calculation method of the similarity coefficient includes a number product method, an included angle cosine method, a correlation coefficient method, an index similarity coefficient method, a maximum and minimum method, a calculation average minimum method, a geometric average minimum method, an absolute value index method, and the like, and in this embodiment, an absolute value subtraction method is used to calculate the similarity coefficient; and taking the calculated similarity coefficients as matrix elements to obtain a similarity matrix R.
As shown in FIG. 5, the propagation closure matrix R is obtained by the flat method16And R is16＝R8(ii) a Then, clustering analysis is carried out according to a net editing method without transforming a transmission closure matrix R16And directly connecting elements of the transitive closure matrix, the elements of which are larger than the threshold lambda, so that the elements on one line belong to the same class. In this embodiment, the threshold λ is a variable, and the value of λ is 0.91; clustering is performed according to the net-making method, and as shown in FIG. 6, 1-16 in FIG. 6 correspond to U, respectively1-U16A total of 16 oiled paper insulation samples, therefore, the classification results of the netting method are: { U1，U2，U3，U4}，{U5，U6，U7，U8，U9}，{U10，U11，U12，U13}，{U14，U15，U16The preset classification result is { U }1，U2，U3，U4}，{U5，U6，U7，U8}，{U9，U10，U11，U12}，{U13，U14，U15，U16Therefore, the accuracy of the cluster analysis in this embodiment is 14/16, i.e. 87.5%.
It should be noted that there are many factors that affect the accuracy of the cluster analysis, such as: the oil paper insulation heat aging experiment, the measurement error of the raman spectrum, the moisture content in the oil paper insulation sample, and the like, so that the accuracy of the cluster analysis in this embodiment can only explain the above identification method, and cannot represent the theoretical correct probability.
The examples provided are only for illustrating the concept and method of the present invention and do not limit the scope of the present invention. Any other embodiments extended by the solution according to the invention without inventive step will be within the scope of protection of the invention for a person skilled in the art.
Claims (9)
1. The method for identifying the aging state of the oil paper insulation equipment based on Raman spectrum cluster analysis is characterized by comprising the following steps:
obtaining oil paper insulation samples of a plurality of known aging stages through an oil paper insulation thermal aging experiment;
acquiring Raman spectrum data of a plurality of oiled paper insulation samples in the known aging stage, and carrying out standardization treatment;
extracting the Raman spectrum characteristic quantity of the oil paper insulation sample in the known aging stage after standardized treatment by adopting a principal component analysis method, and establishing an oil paper insulation aging evaluation model based on a cluster analysis method;
obtaining oil paper insulation samples of a plurality of unknown aging stages through an oil paper insulation thermal aging experiment;
acquiring Raman spectrum data of the oil paper insulation samples in the unknown aging stages, and carrying out standardization treatment;
and extracting the Raman spectrum characteristic quantity of the oil paper insulation sample in the unknown aging stage after the standardization treatment by adopting a principal component analysis method, and inputting the Raman spectrum characteristic quantity into the oil paper insulation aging evaluation model for clustering analysis to obtain the aging stage of the oil paper insulation sample in the unknown aging stage.
2. The method for identifying the aging state of the oiled paper insulation equipment based on the Raman spectral cluster analysis according to claim 1, wherein the step of obtaining oiled paper insulation samples at a plurality of known aging stages through an oiled paper insulation thermal aging experiment comprises the following steps:
drying Clarithrome No. 25 mineral oil and 0.2mm insulating paper in a vacuum oven at 90 deg.C for 48 hr;
adjusting the temperature of the vacuum box to 60 ℃, and soaking the insulation paper in the mineral oil Clarity No. 25 for 24 hours;
moving the oiled paper insulation sample into a vacuum heating tank filled with nitrogen;
the heating tank was placed in a constant temperature oil bath at 130 ℃ and subjected to accelerated thermal aging for 25 days.
3. The method for identifying the aging state of the oiled paper insulation equipment based on Raman spectral cluster analysis according to claim 2, wherein the moisture content of the Clarity 25 mineral oil is 10mg/kg or less, and the moisture content of the insulation paper is 0.5% or less.
4. The method for identifying the aging state of the oiled paper insulation equipment based on Raman spectral cluster analysis according to claim 2, wherein the mass ratio of the insulation paper to the Clarity No. 25 mineral oil is 1: 10.
5. The method for identifying the aging state of the oiled paper insulation equipment based on the Raman spectral cluster analysis according to claim 1, wherein the standardization processing formula is as follows:
wherein x is*Is normalized Raman spectrum data value, x is original Raman spectrum data value, xminIs the minimum value, x, of the Raman spectral datamaxIs the maximum of the raman spectral data.
6. The method for identifying the aging state of the oiled paper insulation equipment based on the Raman spectral cluster analysis according to claim 1, wherein the operation method of the principal component analysis method is as follows:
performing linear fitting on a plurality of independent variables contained in the original Raman spectrum data by using a maximum variance principle;
and replacing the original high-dimensional variable with the new low-dimensional variable to realize the dimension reduction of the Raman spectrum data.
7. The method for identifying the aging state of the oiled paper insulation equipment based on the Raman spectral cluster analysis according to claim 1, wherein the step of establishing the oiled paper insulation aging evaluation model comprises the following steps:
extracting n Raman spectrum characteristic quantities of the oiled paper insulation sample at the known aging stage by a principal component analysis method, and modeling;
performing cluster analysis by taking known different aging stages as row variables and n Raman spectrum characteristic quantities as column variables;
and clustering according to a net-editing method, and connecting elements which are larger than a threshold lambda in the transmission closure matrix according to a clustering principle, so that the elements on one line belong to the same class.
8. The method for identifying the aging state of the oiled paper insulation equipment based on Raman spectrum cluster analysis according to claim 7, wherein the process of cluster analysis is as follows:
let U be the paper oil insulation samples of m known aging stages to be classified, wherein each paper oil insulation sample of the known aging stages to be classified is characterized by a set of data, and the formula is as follows:
Ui＝(xi1+xi2+xi3+...+xin)，
wherein, UiDenotes the ith oiled paper insulation sample, xi11 st Raman spectrum characteristic quantity, x, of the ith oiled paper insulation samplei22 nd Raman spectrum characteristic quantity, x, of the ith oiled paper insulation samplei3Represents the 3 rd Raman spectrum characteristic quantity, x of the ith oiled paper insulation sampleinRepresenting the nth Raman spectrum characteristic quantity of the ith oiled paper insulation sample;
the similarity relation is established by using an absolute value subtraction method, and the method for calculating the similarity coefficient comprises the following steps:
rij＝1,i＝j；
wherein i and j are the numbers of the oiled paper insulation samples, rijThe similarity coefficient of the ith oiled paper insulation sample and the jth oiled paper insulation sample is shown, c is a constant, xikIs the k Raman spectrum characteristic quantity, x, of the ith oiled paper insulation samplejkThe kth Raman spectrum characteristic quantity of the jth oilpaper insulation sample is obtained;
taking the similarity coefficient of the oiled paper insulation samples in each known aging stage as an element of a similarity matrix, and obtaining a similarity matrix R;
method for solving transmission closure matrix R by using flat method16The calculation formula is as follows:
R2＝R*R＝∨(R(a,b)∧R(b,c))，
wherein a, b and c all represent elements in the similarity matrix R, and R can be obtained by the same method4，R8，R16。
9. The method for identifying the aging state of the oiled paper insulation equipment based on Raman spectrum cluster analysis according to claim 1, wherein the cluster analysis of the Raman spectrum characteristic quantity of the oiled paper insulation sample at the unknown aging stage specifically comprises:
adding the Raman spectrum characteristic quantity of the oiled paper insulation sample at the unknown aging stage into an oiled paper insulation aging evaluation model as a new line variable;
performing cluster analysis on the new row variable based on a cluster analysis method;
and obtaining the aging stage of the oiled paper insulation sample in the unknown aging stage according to the clustering result.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112129741A (en) * | 2020-10-10 | 2020-12-25 | 广东电网有限责任公司广州供电局 | Insulating oil aging analysis method and device, computer equipment and storage medium |
| CN112485609A (en) * | 2020-10-19 | 2021-03-12 | 重庆大学 | Raman spectrum diagnosis method for insulation aging of transformer oil paper |
| CN112557834A (en) * | 2020-10-19 | 2021-03-26 | 重庆大学 | Aging diagnosis method for oiled paper insulation equipment based on Raman spectrum |
| CN113985218A (en) * | 2021-09-20 | 2022-01-28 | 重庆大学 | Oil paper insulation aging diagnosis method based on fluorescent color |
| CN113987909A (en) * | 2021-09-18 | 2022-01-28 | 广东电网有限责任公司广州供电局 | Oil paper insulation aging prediction method and device, computer equipment and storage medium |
| CN114088660A (en) * | 2021-11-10 | 2022-02-25 | 国网安徽省电力有限公司电力科学研究院 | Insulating paper water content evaluation method based on robust wavelength screening |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512474A (en) * | 2015-12-02 | 2016-04-20 | 国网山东省电力公司电力科学研究院 | Transformer state monitoring data anomaly detection method |
| CN106885978A (en) * | 2017-04-20 | 2017-06-23 | 重庆大学 | A kind of paper oil insulation Diagnosis of Aging based on insulating oil Raman spectrum wavelet-packet energy entropy |
| CN107843718A (en) * | 2017-10-20 | 2018-03-27 | 广东电网有限责任公司河源供电局 | A kind of method that transformer insulation oil ageing state is assessed |
| CN108564254A (en) * | 2018-03-15 | 2018-09-21 | 国网四川省电力公司绵阳供电公司 | Controller switching equipment status visualization platform based on big data |
| CN108594048A (en) * | 2018-06-19 | 2018-09-28 | 电子科技大学 | A kind of composite insulator degree of aging appraisal procedure based on laser irradiation |
| CN109063400A (en) * | 2018-10-17 | 2018-12-21 | 苏州科技大学 | Generator stator bar insulating thermal aging lifetime estimation method based on statistical check |
| CN109406898A (en) * | 2018-11-07 | 2019-03-01 | 福州大学 | A method of fusion multi-characteristicquantity quantity comprehensive assessment paper oil insulation degree of aging |
| CN110031443A (en) * | 2019-04-15 | 2019-07-19 | 重庆大学 | A kind of portable oil paper insulation ageing state Raman spectrum diagnostic device and method |
| CN110488164A (en) * | 2019-08-22 | 2019-11-22 | 中国电力科学研究院有限公司 | A kind of high-voltage cable insulating ageing state comprehensive assessment method for early warning and system |
| CN110567937A (en) * | 2019-09-11 | 2019-12-13 | 重庆大学 | Competitive self-adaptive heavy-weighted key data extraction method for Raman spectrum analysis of insulating oil |
-
2020
- 2020-04-26 CN CN202010339649.1A patent/CN111537845A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512474A (en) * | 2015-12-02 | 2016-04-20 | 国网山东省电力公司电力科学研究院 | Transformer state monitoring data anomaly detection method |
| CN106885978A (en) * | 2017-04-20 | 2017-06-23 | 重庆大学 | A kind of paper oil insulation Diagnosis of Aging based on insulating oil Raman spectrum wavelet-packet energy entropy |
| CN107843718A (en) * | 2017-10-20 | 2018-03-27 | 广东电网有限责任公司河源供电局 | A kind of method that transformer insulation oil ageing state is assessed |
| CN108564254A (en) * | 2018-03-15 | 2018-09-21 | 国网四川省电力公司绵阳供电公司 | Controller switching equipment status visualization platform based on big data |
| CN108594048A (en) * | 2018-06-19 | 2018-09-28 | 电子科技大学 | A kind of composite insulator degree of aging appraisal procedure based on laser irradiation |
| CN109063400A (en) * | 2018-10-17 | 2018-12-21 | 苏州科技大学 | Generator stator bar insulating thermal aging lifetime estimation method based on statistical check |
| CN109406898A (en) * | 2018-11-07 | 2019-03-01 | 福州大学 | A method of fusion multi-characteristicquantity quantity comprehensive assessment paper oil insulation degree of aging |
| CN110031443A (en) * | 2019-04-15 | 2019-07-19 | 重庆大学 | A kind of portable oil paper insulation ageing state Raman spectrum diagnostic device and method |
| CN110488164A (en) * | 2019-08-22 | 2019-11-22 | 中国电力科学研究院有限公司 | A kind of high-voltage cable insulating ageing state comprehensive assessment method for early warning and system |
| CN110567937A (en) * | 2019-09-11 | 2019-12-13 | 重庆大学 | Competitive self-adaptive heavy-weighted key data extraction method for Raman spectrum analysis of insulating oil |
Non-Patent Citations (3)
| Title |
|---|
| 宋世德编著: "《简明模糊数学基础》", 31 January 2016, 西北农林科技大学出版社 * |
| 祝顺才等: "基于模糊-灰色聚类的油纸绝缘状态综合诊断", 《仪器仪表学报》 * |
| 邹经鑫: "油纸绝缘老化拉曼光谱特征量提取及诊断方法研究", 《中国优秀博硕士学位论文全文数据库（博士） 工程科技II辑》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112129741A (en) * | 2020-10-10 | 2020-12-25 | 广东电网有限责任公司广州供电局 | Insulating oil aging analysis method and device, computer equipment and storage medium |
| CN112485609A (en) * | 2020-10-19 | 2021-03-12 | 重庆大学 | Raman spectrum diagnosis method for insulation aging of transformer oil paper |
| CN112557834A (en) * | 2020-10-19 | 2021-03-26 | 重庆大学 | Aging diagnosis method for oiled paper insulation equipment based on Raman spectrum |
| CN112485609B (en) * | 2020-10-19 | 2021-11-23 | 重庆大学 | Raman spectrum diagnosis method for insulation aging of transformer oil paper |
| CN113987909A (en) * | 2021-09-18 | 2022-01-28 | 广东电网有限责任公司广州供电局 | Oil paper insulation aging prediction method and device, computer equipment and storage medium |
| CN113985218A (en) * | 2021-09-20 | 2022-01-28 | 重庆大学 | Oil paper insulation aging diagnosis method based on fluorescent color |
| CN113985218B (en) * | 2021-09-20 | 2024-04-12 | 重庆大学 | Fluorescent color-based oiled paper insulation aging diagnosis method |
| CN114088660A (en) * | 2021-11-10 | 2022-02-25 | 国网安徽省电力有限公司电力科学研究院 | Insulating paper water content evaluation method based on robust wavelength screening |
| CN114088660B (en) * | 2021-11-10 | 2023-11-24 | 国网安徽省电力有限公司电力科学研究院 | Insulation paper water content evaluation method based on robust wavelength screening |
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